The present invention relates to techniques for characterizing the response of biological cells to varying levels of a particular stimulus. More specifically, the invention relates to techniques for measuring similarities/dissimilarities between stimulus response curves through, for example, multivariate phenotypic signatures extracted from images of biological cells. The similarity of two such curves can be calculated by measuring a “distance” between them. As explained elsewhere herein, the concept of distance includes both conventional measures of “true” distance such as a Euclidean distance as well as other measures of dissimilarity such as a degree of orthogonality (as with an inner product). Generally, the “distances” characterize the similarity of the stimuli generating the response curves.
Purified substances having a desirable combination of bioactive properties are rare and often difficult to identify. Recent advances in organic chemistry and the development of rapid combinatorial chemistry techniques have increased the number of compounds that researchers can test for a specific biological activity (e.g., binding to a target). Unfortunately, the vast majority of “hits” generated by such techniques do not possess the right combination of properties to qualify as therapeutic compounds. When these substances are subjected to low throughput cellular and animal tests to establish their therapeutic usefulness, they are typically found to fail in some regard. Unfortunately, such tests are time consuming and costly, thus limiting the number of substances that can be tested. In a like regard, the few hits that do possess the right combination of properties avoid recognition until after the low throughput tests are conducted. With better early evaluation techniques, such promising candidates could be identified earlier in the development process and put on a fast track to the marketplace.
Various early evaluation techniques are under investigation and some have shown promise. In particular cellular phenotyping technologies employing sophisticated image analysis have proven very useful in characterizing therapeutic chemicals. Such technologies are generally described in WO/00/70528 published on Nov. 23, 2000. These techniques attempt to classify compounds based on the phenotypic changes that they induce. From these changes, detailed mechanisms of action can be deduced.
Typically, researchers attempting to classify a new compound based on mechanism of action, toxicity, etc. compare features of that compound to known therapeutics. Compounds that exhibit similar biological functioning in some regards may exhibit similarity in other regards. One difficulty in assessing similarity is that compounds often have greatly varying potencies. In other words, while two different compounds may operate by the same or similar mechanism of action, one compound may be effective at a much lower concentration than the other. It is difficult to make meaningful comparison of two such compounds until the dose scales of these compounds have been adjusted. To this end, researchers sometimes use dose response curves to compare compounds. These curves show the biological effectiveness of particular drugs over multiple concentrations. The effect of the drug at each different concentration provides the “points” for the dose response curves.
Typically, such dose response curves are limited to one particular biological feature (e.g., cell count or expression of a protein). The numeric value of such feature is provided as a function of concentration for each compound of interest. The resulting curves can be compared to identify similar trajectories. Two compounds having similar trajectories might be expected to operate by the same mechanism of action, depending upon which biological parameter is being considered. Unfortunately, the value of such comparisons is quite limited. First, many different features may be required to unambiguously characterize a stimulus' mechanism of action. So a simple dose response curve may be inconclusive. Second, even assuming that all the relevant features are captured in the analysis, determining whether the response of an unclassified stimulus more properly belongs to one mechanism or another can be difficult. It requires a trustworthy technique for classification or clustering responses in biological feature space. This, in turn, requires a trustworthy technique for calculating “distances” between the responses in biological feature space.
While many techniques for characterizing compounds and other biological stimuli exist, their full potential for classifying such stimuli by mechanism of action has not yet been realized. Advances will require rigorous and reliable techniques for calculating “distances” between stimulus response curves, such as dose response trajectories for compounds or other stimuli under investigation.